Media Alert

REDWOOD CITY, CA — November 14, 2016 — Numerous proposals have been offered
for how intelligent machines might learn sequences of patterns, which is
believed to be an essential component of any intelligent system. Researchers at
Numenta Inc. have published a new study, “Continuous Online Sequence
Learning with an Unsupervised Neural Network Model,” which compares their
biologically-derived HTM sequence memory to traditional machine learning
algorithms.

The paper has been published in MIT Press Journal’s Neural Computation 28,
2474–2504 (2016). You can read and download the paper here.

The earlier paper described a biological theory of how networks of neurons in
the neocortex learn sequences. In this paper, the authors demonstrate how this
theory, HTM sequence memory, can be applied to sequence learning and prediction
of streaming data.

“Our primary goal at Numenta is to understand, in detail, how the neocortex
works. We believe the principles we learn from the brain will be essential for
creating intelligent machines, so a second part of our mission is to bridge the
two worlds of neuroscience and AI. This new work demonstrates progress towards
that goal,” Hawkins commented.

The results in this paper show that HTM sequence memory achieves comparable
prediction accuracy to these other techniques. However, the HTM model also
exhibits several properties that are critical for streaming data applications
including:

Continuous online learning

Ability to make multiple simultaneous predictions

Robustness to sensor noise and fault tolerance

Good performance without task-specific tuning

“Many existing machine learning techniques demonstrate some of these
properties,” Cui noted, “but a truly powerful system for streaming analytics
should have all of them.”

The HTM sequence memory algorithm is something that machine learning experts can
test and incorporate into a broad range of applications. In keeping with
Numenta’s open research philosophy, the source code for replicating the
graphs in the paper can be found here. Numenta also welcome questions and
discussion about the paper on the HTM Forum or by contacting the authors
directly.

About Neural Computation

Neural Computation disseminates important, multidisciplinary research results
in a field that attracts psychologists, physicists, computer scientists,
neuroscientists, and artificial intelligence investigators, among others. For
researchers looking at the scientific and engineering challenges of
understanding the brain and building computers, Neural Computation highlights
common problems and techniques in modeling the brain, and in the design and
construction of neurally-inspired information processing systems.

About Numenta

Founded in 2005, Numenta develops theory, software technology, and applications
all based on reverse engineering the neocortex. Laying the groundwork for the
new era of machine intelligence, this technology is ideal for analysis of
continuously streaming data and excels at modeling and predicting patterns in
data. Numenta has also developed a suite of products and demonstration
applications that utilize its flexible and generalizable
Hierarchical Temporal Memory (HTM) learning algorithms to provide solutions
that encompass the fields of machine generated data, human behavioral modeling,
geo-location processing, semantic understanding and sensory-motor control. In
addition, Numenta has created
NuPIC (Numenta Platform for Intelligent Computing) as an open source
project. Numenta is based in Redwood City, California.